1. Thank you for your invitation and kind hospitality !
Isaac Held, Beijing, 2011
Thank you for your invitation and kind hospitality !
Seminars:
Monday: Time Scales of Global Warming
Tuesday: Simulating the climatology, interannual variability,
and trends of tropical cyclone genesis
Wednesday: The hydrological cycle and global warming
Thursday: Shifting latitude of surface westerlies –
a case study in utilizing a hierarchy of climate models
(understanding climate by starting with comprehensive models
and gradually removing layers of complexity)
Friday: Problems in quasi-geostrophic dynamics
(understanding climate by starting with very idealized models
and gradually adding layers of complexity)

2. Time scales of climate responses, climate sensitivity,
and the recalcitrant component of global warming
Isaac Held
Beijing, 2011
Importance of Ocean Heat Uptake Efficacy to Transient Climate Change
Winton, Takahashi, Held, J. Clim, 2010
Probing the fast and slow components of global warming by returning abruptly to pre-industrial forcing
Held, Winton, Takahashi, Delworth, Zeng, Vallis, J. Clim 2010

3. Uncertainty in climate sensitivity
has not been reduced appreciably in past 30 years
2 well-known assessments reach similar conclusions :
“Charney report” (1979)  IPCC/AR4 (2006)
Equilibrium global mean surface temperature warming
due to doubling of CO2
is most probably in the range K

4. Assorted estimates of equilibrium sensitivity
Knutti+Hegerl, 2008 23

5. Time scales of climate response
Ultra-fast
Fast
Slow
Ultra-slow
Months
(Atmosphere)
a few years
(mixed layer)
Multiple centuries
(deep ocean)

6. t Equilibrium climate sensitivity:
Double the CO2 and wait for the system to equilibrate
Transient climate response:
Increase CO2 1%/yr and examine climate at the time of doubling
Typical setup – increase till doubling – then hold constant
CO2 forcing
T response
W/m2 t
Heat uptake by deep ocean
After CO2 stabilized, warming of near surface
can be thought of as due to reduction in heat uptake 11

7. CMIP3/AR4 models 2.5 2 Transient response 1.5 1 2 3 4 5
Equilibrium sensitivity
Not well correlated across models – equilibrium response brings into play
feedbacks/dynamics (especially in subpolar oceans)
that are suppressed in transient response 19

8. Histogram of TCR/TEQ for AR4 models
Increase CO2 by 1%/yr ; global mean warming at the time of doubling
= Transient Climate Response (TCR)

9. Response of global mean temperature in GFDL’s CM2.1
to instantaneous doubling of CO2
Equilibrium sensitivity 3.4K
Transient response 1.5K
Slow response
evident only
after 80 yrs
Fast response 20

10. forcing
Mixed layer
Heat capacity
Heat exchange
between mixed layer
and deep ocean
Deep ocean
heat capacity
in equilibrium

11. Forcing varies on time scales longer than

12. “Intermediate regime”
Forcing varies on time scales longer than
and time scales shorter than
“Intermediate regime”

13. Forcing computed from differencing TOA fluxes in two runs of a model (B-A)
B = fixed SSTs with varying forcing agents; A fixed SSTs and fixed forcing agents
total
OLR
SW down 51
SW up

14. Temperature change averaged over 5 realizations of coupled model52

15. Fit with 53

16. Forcing (with no damping) fits the trend well, if you use
transient climate sensitivity,
which takes into account
magnitude/efficacy of heat uptake
Forcing with no damping 54

18. GFDL’s CM2.1 with well-mixed greenhouse gases only
Global mean
temperature
change
Observations
(GISS) 46

19. GFDL’s CM2.1 with well-mixed greenhouse gases only
Global mean
temperature
change
Observations
(GISS)
“It is likely that increases in greenhouse gas concentrations alone
would have caused more warming than observed because
volcanic and anthropogenic aerosols have offset some warming
that would otherwise have taken place.” (AR4 WG1 SPM). 46

20. A1B-CM2.1

21. Return instantaneously to pre-industrial forcing
the “Recalcitrant” warming

22. Relaxation to recalcitrant warming
5 years
3 years

23. Normalized to unity over the globe

24. Normalized to unity over the globe
Fast
Slow
“Recalcitrant”

25. Sea level response mostly recalcitrant
Sea level response due to thermal expansion
Control drift
Sea level response mostly recalcitrant

26. The simplest linear model
If correct, evolution should be along the diagonal
N/F
T/TEQ 15

28. Suppose you have two forcing agents C02 and B (something else)
leading to radiative forcing FC02 and FB .
But suppose the global mean temperature responses TC02 and TB
are not proportional to the the radiative forcing
Following Hansen, define efficacy eB (using CO2 as a standard)

29. Efficacy can orten be understood in terms of
the spatial structure of the response ,
Coupling of surface with troposphere is weaker in high latitudes
=> harder to radiate away a perturbation
=> Radiative restoring strength is weaker for responses that
are larger in higher latitudes
=> Forcings with stronger high latitude responses have larger efficacy

30. replace F = bT + H or bT = F + H with bT = F + eH H with eH > 1
Forcings with stronger high latitude responses have larger efficacy
Think of heat uptake as a forcing – ie
replace F = bT + H or bT = F + H
with bT = F + eH H with eH > 1
Equivalently,
T = TF + TH = F/b - H/bH
With bH = b/eH

31. Efficiency Efficacy Heat uptake = gT ; g = efficiency of heat uptake
Cooling due to heat uptake = egT ; e = efficacy of heat uptake
Efficiency
CM 2.1
CM 2.0
Efficacy

32. Assorted estimates of equilibrium sensitivity
Knutti+Hegerl, 2008 23

33. Models can produce very good fits by including aerosol effects,
(GFDL CM Includes estimates of volcanic and anthropogenic aerosols,
as well as estimates of variations in solar irradiance)
Models can produce very good fits by including aerosol effects,
but models with
stronger aerosol forcing and higher climate sensitivity
are also viable (and vice-versa) 45

34. Internal Fluctuations Seasonal cycle etc
Observational constraints
20th century warming
1000yr record
Ice ages – LGM
Deep time
Volcanoes
Solar cycle
Internal Fluctuations
Seasonal cycle etc 36

35. Observed total solar irradiance variations in 11yr solar cycle (~ 0
Observed total solar irradiance variations in 11yr solar cycle (~ 0.2% peak-to-peak) 42

36. (other studies yield ~0.1K) Seems to imply large sensitivity
Camp and Tung, =>
0.2K peak to peak
(other studies yield ~0.1K)
Seems to imply large
sensitivity
4 yr damping time
Only gives
0.05 peak to peak
1.8K (transient) sensitivity 43

37. Global mean cooling due to Pinatubo volcanic eruption
Observations
with
El Nino
removed
Range of
~10 Model
Simulations
GFDL CM2.1
Courtesy of G Stenchikov 40
Relaxation time after abrupt cooling contains information on climate sensitivity

38. Low sensitivity model
Pinatubo simulation
High sensitivity model
Yokohata, et al, 2005 41

39. Response to pulse of forcing (volcano), F(t):
2-box model:

40. Stenchikov, et al 2009

42. Near surface air temperature response (20 member ensemble)
Courtesy of Stenchikov, et al

44. Integrated forcing and response
Wm-2yr
Response
with exponential fit
TOA flux
Forcing

45. CM2.1 Pinatubo summary
-- fast response --

46. 2.8 5.0 2.2 CM2.1 Pinatubo summary -- fast response --
Radiative restoring
(W/m2)yr
2.8
Forcing (W/m2)yr
5.0
Heat uptake (W/m2)yr
2.2
CM2.1 Pinatubo summary
-- fast response --

47. Pinatubo =>
b ~ 1.0 (W/m2)/K
g ~ 0.8 (W/m2)/K
1%/yr CO2 increase =>
b ~ 1.7 (W/m2)/K
g ~ 0.7 (W/m2)/K

48. Can we use interannual variability to determine
the strength of the radiative restoring?
Model results (CM2.1) raise some roadblocks

49. bLW Longwave regression across ensemble Wm-2K-1
(collaboration with K. Swanson)
All-forcing
20th century
bLW
Wm-2K-1
year 61
Following an idea of K. Swanson,
take a set of realizations of the 20th century from one model,
and correlate global mean TOA with surface temperature across the ensemble

50. bLW Longwave regression across ensemble, Wm-2K-1
collaboration with K. Swanson
All-forcing
20th century
A1B scenario
bLW
Wm-2K-1 62

51. bLW Longwave regression across ensemble, Wm-2K-1
collaboration with K. Swanson
bLW
Wm-2K-1 63
Estimate of noise in this statistic from 2000yr control run

52. bLW Longwave regression across ensemble, Wm-2K-1
collaboration with K. Swanson
bLW
Wm-2K-1
Well-mixed
greenhouse gases only 64

53. bLW Longwave regression across ensemble, Wm-2K-1
collaboration with K. Swanson
bLW
Wm-2K-1
Independent set of 10
A1B runs 65

54. bLW Longwave regression across ensemble,
collaboration with K. Swanson
But we can fit the models 20th century simulations
without time-dependence in OLR-temperature relationship!
bLW
Wm-2K-1
Independent set of 10
A1B runs 65
May be telling us that ENSO is changing,
but with no obvious connection to global sensitivity

55. Thank you for listening
I suspect that:
Transient climate sensitivity can be constrained more tightly that it currently is, despite the uncertainty in aerosol forcing
Volcanic responses may play a central role in tightening this constraint, along with the observed
warming trend
Less hopeful about use of interannual variability
Solar cycle response has some mysteries
Thank you for listening

56. 21st century emissions commitment AR4/IPCC

57. Wm-2K-1 Shortwave regression across ensemble,
following K. Swanson 2008
All-forcing
20th century
Wm-2K-1 56
Following an idea of K. Swanson,
take a set of realizations of the 20th century from one model,
and correlate global mean TOA with surface temperature across the ensemble

58. Wm-2K-1 Shortwave regression across ensemble,
following K. Swanson 2008
A1B scenario
All-forcing
20th century
Wm-2K-1 57
Is this a sign of non-linearity? What is this?

59. Wm-2K-1 Shortwave regression across ensemble,
following K. Swanson 2008
A1B scenario
All-forcing
20th century
Wm-2K-1
90% 58
Estimate of noise in this statistic from 2000yr control run

60. Wm-2K-1 Shortwave regression across ensemble,
following K. Swanson 2008
Well-mixed
greenhouse gases only
Wm-2K-1 59

61. Wm-2K-1 Shortwave regression across ensemble,
following K. Swanson 2008
Independent set of 10
A1B runs
Wm-2K-1 60